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Life testing or test-to-failure method and binomial testing method are the two most commonly used methods in product validation and reliability demonstration. The two-parameter Weibull distribution function is often used in the life testing and almost exclusively used in the extended time testing, which can be considered as an accelerated testing method by appropriately extending the testing time but with significantly reduced testing samples. However, the fatigue data from a wide variety of sources indicate that the three-parameter Weibull distribution function with a threshold parameter at the left tail is more appropriate for fatigue life data with large sample sizes. The uncertainties introduced from the assumptions about the underlying probabilistic distribution would significantly affect the interpretation of the test data and the assessment of the performance of the accelerated binomial testing methods, therefore, the selection of a probabilistic model is critically important.

Product validation and reliability demonstration require testing of limited samples and probabilistic analyses of the test data. The uncertainties introduced from the tests with limited sample sizes and the assumptions made about the underlying probabilistic distribution will significantly impact the results and the results interpretation. Therefore, understanding the nature of these uncertainties is critical to test method development, uncertainty reduction, data interpretation, and the effectiveness of the validation and reliability demonstration procedures. In this paper, these uncertainties are investigated with the focuses on the following two aspects: (1) fundamentals of the RxxCyy criterion used in both the life testing and the binomial testing methods, (2) issues and benefits of using the two-parameter Weibull probabilistic distribution function.

A statistic fatigue life assessment procedure is presented in this paper for estimating fatigue damage under stationary Gaussian multi-axial loadings with narrow-band frequency. The fatigue damage is determined by using the Miner-Palmgren rule in connection with a recently developed path-length based multi-axial cycle counting and fatigue life assessment method. In this procedure, the path length is determined by averaging sinusoidal waves with uniformly distributed phase angles while the cycles are estimated from the observation of peak counting results of stress components. Numerically simulated random loading paths with different degrees of non-proportionality are used here to compare the proposed statistical method with its time domain counterpart. Possible further improvement in this research direction is also indicated.